Published on 15.07.15 in Vol 17, No 7 (2015): July
Works citing "Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study"
According to Crossref, the following articles are citing this article (DOI 10.2196/jmir.4273):
(note that this is only a small subset of citations)
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Razavi R, Gharipour A, Gharipour M. Depression screening using mobile phone usage metadata: a machine learning approach. Journal of the American Medical Informatics Association 2020;27(4):522
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Masud MT, Mamun MA, Thapa K, Lee D, Griffiths MD, Yang S. Unobtrusive monitoring of behavior and movement patterns to detect clinical depression severity level via smartphone. Journal of Biomedical Informatics 2020;103:103371
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Wang R, Wang W, Aung MSH, Ben-Zeev D, Brian R, Campbell AT, Choudhury T, Hauser M, Kane J, Scherer EA, Walsh M. Predicting Symptom Trajectories of Schizophrenia using Mobile Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2017;1(3):1
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Majumder S, Deen MJ. Smartphone Sensors for Health Monitoring and Diagnosis. Sensors 2019;19(9):2164
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Aung MH, Matthews M, Choudhury T. Sensing behavioral symptoms of mental health and delivering personalized interventions using mobile technologies. Depression and Anxiety 2017;34(7):603
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Becker D, van Breda W, Funk B, Hoogendoorn M, Ruwaard J, Riper H. Predictive modeling in e-mental health: A common language framework. Internet Interventions 2018;12:57
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Rashid H, Mendu S, Daniel KE, Beltzer ML, Teachman BA, Boukhechba M, Barnes LE. Predicting Subjective Measures of Social Anxiety from Sparsely Collected Mobile Sensor Data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2020;4(3):1
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Cornet VP, Holden RJ. Systematic review of smartphone-based passive sensing for health and wellbeing. Journal of Biomedical Informatics 2018;77:120
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Aung MSH, Alquaddoomi F, Hsieh C, Rabbi M, Yang L, Pollak JP, Estrin D, Choudhury T. Leveraging Multi-Modal Sensing for Mobile Health: A Case Review in Chronic Pain. IEEE Journal of Selected Topics in Signal Processing 2016;10(5):962
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Fraccaro P, Beukenhorst A, Sperrin M, Harper S, Palmier-Claus J, Lewis S, Van der Veer SN, Peek N. Digital biomarkers from geolocation data in bipolar disorder and schizophrenia: a systematic review. Journal of the American Medical Informatics Association 2019;26(11):1412
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Fairburn CG, Patel V. The impact of digital technology on psychological treatments and their dissemination. Behaviour Research and Therapy 2017;88:19
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Boukhechba M, Daros AR, Fua K, Chow PI, Teachman BA, Barnes LE. DemonicSalmon: Monitoring mental health and social interactions of college students using smartphones. Smart Health 2018;9-10:192
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Palmer KM, Burrows V. Ethical and Safety Concerns Regarding the Use of Mental Health–Related Apps in Counseling: Considerations for Counselors. Journal of Technology in Behavioral Science 2021;6(1):137
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Asselbergs J, Ruwaard J, Ejdys M, Schrader N, Sijbrandij M, Riper H. Mobile Phone-Based Unobtrusive Ecological Momentary Assessment of Day-to-Day Mood: An Explorative Study. Journal of Medical Internet Research 2016;18(3):e72
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Saha K, Chan L, De Barbaro K, Abowd GD, De Choudhury M. Inferring Mood Instability on Social Media by Leveraging Ecological Momentary Assessments. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2017;1(3):1
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Berrouiguet S, Ramírez D, Barrigón ML, Moreno-Muñoz P, Carmona Camacho R, Baca-García E, Artés-Rodríguez A. Combining Continuous Smartphone Native Sensors Data Capture and Unsupervised Data Mining Techniques for Behavioral Changes Detection: A Case Series of the Evidence-Based Behavior (eB2) Study. JMIR mHealth and uHealth 2018;6(12):e197
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Harari GM, Müller SR, Mishra V, Wang R, Campbell AT, Rentfrow PJ, Gosling SD. An Evaluation of Students’ Interest in and Compliance With Self-Tracking Methods. Social Psychological and Personality Science 2017;8(5):479
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Kamilaris A, Pitsillides A. Mobile Phone Computing and the Internet of Things: A Survey. IEEE Internet of Things Journal 2016;3(6):885
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Brietzke E, Hawken ER, Idzikowski M, Pong J, Kennedy SH, Soares CN. Integrating digital phenotyping in clinical characterization of individuals with mood disorders. Neuroscience & Biobehavioral Reviews 2019;104:223
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Schneble CO, Elger BS, Shaw DM. All Our Data Will Be Health Data One Day: The Need for Universal Data Protection and Comprehensive Consent. Journal of Medical Internet Research 2020;22(5):e16879
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Cao J, Truong AL, Banu S, Shah AA, Sabharwal A, Moukaddam N. Tracking and Predicting Depressive Symptoms of Adolescents Using Smartphone-Based Self-Reports, Parental Evaluations, and Passive Phone Sensor Data: Development and Usability Study. JMIR Mental Health 2020;7(1):e14045
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Henson P, Barnett I, Keshavan M, Torous J. Towards clinically actionable digital phenotyping targets in schizophrenia. npj Schizophrenia 2020;6(1)
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Kirchner TR, Shiffman S. Spatio-temporal determinants of mental health and well-being: advances in geographically-explicit ecological momentary assessment (GEMA). Social Psychiatry and Psychiatric Epidemiology 2016;51(9):1211
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Tuerk PW, Schaeffer CM, McGuire JF, Adams Larsen M, Capobianco N, Piacentini J. Adapting Evidence-Based Treatments for Digital Technologies: a Critical Review of Functions, Tools, and the Use of Branded Solutions. Current Psychiatry Reports 2019;21(10)
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. Ontology Components for the Depression Management based on Context. Journal of the Korea Institute of Information and Communication Engineering 2016;20(9):1785
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Schoedel R, Au Q, Völkel ST, Lehmann F, Becker D, Bühner M, Bischl B, Hussmann H, Stachl C. Digital Footprints of Sensation Seeking. Zeitschrift für Psychologie 2018;226(4):232
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Price M, Van Stolk-Cooke K, Legrand AC, Brier ZMF, Ward HL, Connor JP, Gratton J, Freeman K, Skalka C. Implementing assessments via mobile during the acute posttrauma period: feasibility, acceptability and strategies to improve response rates. European Journal of Psychotraumatology 2018;9(sup1)
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Dogrucu A, Perucic A, Isaro A, Ball D, Toto E, Rundensteiner EA, Agu E, Davis-Martin R, Boudreaux E. Moodable: On feasibility of instantaneous depression assessment using machine learning on voice samples with retrospectively harvested smartphone and social media data. Smart Health 2020;17:100118
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Sabharwal A, Veeraraghavan A. Bio-Behavioral Sensing. GetMobile: Mobile Computing and Communications 2017;21(3):11
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DeMasi O, Feygin S, Dembo A, Aguilera A, Recht B. Well-Being Tracking via Smartphone-Measured Activity and Sleep: Cohort Study. JMIR mHealth and uHealth 2017;5(10):e137
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Suffoletto B, Aguilera A. Expanding Adolescent Depression Prevention Through Simple Communication Technologies. Journal of Adolescent Health 2016;59(4):373
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Armstrong CM, Ciulla RP, Williams SA, Micheel LJ. An Applied Test of Knowledge Translation Methods Using a Mobile Health Solution. Military Medicine 2020;185(Supplement_1):526
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Hung GC, Yang P, Chang C, Chiang J, Chen Y. Predicting Negative Emotions Based on Mobile Phone Usage Patterns: An Exploratory Study. JMIR Research Protocols 2016;5(3):e160
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Mandryk RL, Birk MV. Toward Game-Based Digital Mental Health Interventions: Player Habits and Preferences. Journal of Medical Internet Research 2017;19(4):e128
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Scott SB, Munoz E, Mogle JA, Gamaldo AA, Smyth JM, Almeida DM, Sliwinski MJ. Perceived neighborhood characteristics predict severity and emotional response to daily stressors. Social Science & Medicine 2018;200:262
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Donker T, Van Esveld S, Fischer N, Van Straten A. 0Phobia – towards a virtual cure for acrophobia: study protocol for a randomized controlled trial. Trials 2018;19(1)
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Chib A, Lin SH. Theoretical Advancements in mHealth: A Systematic Review of Mobile Apps. Journal of Health Communication 2018;23(10-11):909
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Tseng VW, Sano A, Ben-Zeev D, Brian R, Campbell AT, Hauser M, Kane JM, Scherer EA, Wang R, Wang W, Wen H, Choudhury T. Using behavioral rhythms and multi-task learning to predict fine-grained symptoms of schizophrenia. Scientific Reports 2020;10(1)
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Di Matteo D, Fotinos K, Lokuge S, Yu J, Sternat T, Katzman MA, Rose J. The Relationship Between Smartphone-Recorded Environmental Audio and Symptomatology of Anxiety and Depression: Exploratory Study. JMIR Formative Research 2020;4(8):e18751
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Lind MN, Byrne ML, Wicks G, Smidt AM, Allen NB. The Effortless Assessment of Risk States (EARS) Tool: An Interpersonal Approach to Mobile Sensing. JMIR Mental Health 2018;5(3):e10334
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Palmius N, Saunders KEA, Carr O, Geddes JR, Goodwin GM, De Vos M. Group-Personalized Regression Models for Predicting Mental Health Scores From Objective Mobile Phone Data Streams: Observational Study. Journal of Medical Internet Research 2018;20(10):e10194
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Zulueta J, Leow AD, Ajilore O. Real-Time Monitoring: A Key Element in Personalized Health and Precision Health. FOCUS 2020;18(2):175
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Barnett I, Torous J, Staples P, Sandoval L, Keshavan M, Onnela J. Relapse prediction in schizophrenia through digital phenotyping: a pilot study. Neuropsychopharmacology 2018;43(8):1660
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Khan SA, Farhan AA, Fahad LG, Tahir SF. Personal productivity monitoring through smartphones. Journal of Ambient Intelligence and Smart Environments 2020;12(4):327
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Boonstra TW, Nicholas J, Wong QJ, Shaw F, Townsend S, Christensen H. Using Mobile Phone Sensor Technology for Mental Health Research: Integrated Analysis to Identify Hidden Challenges and Potential Solutions. Journal of Medical Internet Research 2018;20(7):e10131
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Porras-Segovia A, Molina-Madueño RM, Berrouiguet S, López-Castroman J, Barrigón ML, Pérez-Rodríguez MS, Marco JH, Díaz-Oliván I, de León S, Courtet P, Artés-Rodríguez A, Baca-García E. Smartphone-based ecological momentary assessment (EMA) in psychiatric patients and student controls: A real-world feasibility study. Journal of Affective Disorders 2020;274:733
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. Mobile Phone Use and Mental Health. A Review of the Research That Takes a Psychological Perspective on Exposure. International Journal of Environmental Research and Public Health 2018;15(12):2692
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Harari GM, Müller SR, Aung MS, Rentfrow PJ. Smartphone sensing methods for studying behavior in everyday life. Current Opinion in Behavioral Sciences 2017;18:83
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Rohani DA, Faurholt-Jepsen M, Kessing LV, Bardram JE. Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review. JMIR mHealth and uHealth 2018;6(8):e165
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